The AI Effect: Working at the Intersection of AI and SE

2024

The rapid advancement of artificial intelligence (AI) technologies has brought about significant ethical considerations that warrant thorough examination. This paper discusses the ethical challenges associated with AI developments, focusing on issues such as bias in AI algorithms, the impact of AI on employment, privacy concerns, and the need for transparency and accountability in AI systems. The discussion emphasizes the importance of developing AI technologies that are not only technically proficient but also ethically sound, highlighting the role of ethical guidelines and frameworks in guiding the responsible development and deployment of AI technologies. Through a qualitative analysis, this study aims to shed light on the implications of AI for society and suggests ways to mitigate potential ethical pitfalls. Privacy Bias Data Collection: Ethical AI development should consider minimizing the collection of personal data to only what is necessary for the intended purpose. Data Bias: Addressing biases in training data that can lead to AI systems making unfair or discriminatory decisions.

ArXiv, 2021

The 3rd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in AI Ethics since October 2020. It aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the field's ever-changing developments. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: algorithmic injustice, discrimination, ethical AI, labor impacts, misinformation, privacy, risk and security, social media, and more. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. Unique to this report is"The Abuse and Misogynoir Playbook,"written by Dr. Katlyn Tuner (Research Scientist, Space Enabled Research Group, MIT), Dr. Danielle Wood...

Cornell University - arXiv, 2021

Go Wide: Article Summaries (summarized by Abhishek Gupta) Ethical AI isn't the same as trustworthy AI, and that matters (Original VentureBeat article by Kimberly Nevala) Google showed us the danger of letting corporations lead AI research (Original QZ article by Nicolás Rivero) If not AI ethicists like Timnit Gebru, who will hold Big Tech accountable? (Original Brookings article by Alex Engler) AI research survey finds machine learning needs a culture change (Original VentureBeat article by Khari Johnson)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Wiener, 1960) (Samuel, 1960). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers 'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. 2

2023

ArXiv, 2020

These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and dev...

International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2025

The emergence of new technologies similar as Artificial Intelligence[AI] has the implicit to disrupt sectors, diligence, governance, and day- to- day life conditioning. Rather than easing societal progress, the advancement of AI comes with increased innovative openings, challenges, and efficiencies to be employed. This systematic research is dedicated to the profound AI ethical dilemmas that touch every and, human values across society. This paper systematically reviews possible ethical dilemmas caused during the development and/or deployment of AI with respect to bias and fairness, transparency and explainability, accountability and responsibility, privacy and surveillance, and human autonomy. Using algorithm-based systems comes with the danger of “black box” opaque decision making. These gaps are particularly problematic in autonomous systems where the traditional concepts of legal and moral liability become incredibly difficult to assign which includes critical domains like healthcare and transportation. This paper analyzes the OECD [Organisation for Economic Co-operation and Development] AI Principles, EU (European Union) Ethics guidelines for trustworthy AI and also the uses of GDPR where AI Ethic Governance models of Right to Explanations.It critically analyzes the strengths and limitations of these approaches, relating significant gaps in perpetration, enforcement, and global adjustment. The exploration highlights the pressure between invention and regulation, demonstrating how current tone-nonsupervisory measures frequently fall suddenly of addressing systemic pitfalls.

AI and Ethics, 2020

Artificial Intelligence (AI) can and does have unintended negative consequences for businesses if not implemented with care. Specifically, faulty or biased AI applications risk compliance and governance breaches and damage to the corporate brand. These issues commonly arise from pitfalls associated with AI development, which include rushed development, a lack of technical understanding, and improper quality assurance, among other factors. To mitigate these risks, a growing number of organisations are working on ethical AI principles and frameworks. However, ethical AI principles alone are not sufficient to ensure responsible AI usage in enterprises. Businesses also require strong, mandated governance controls including tools for managing processes and creating associated audit trails to enforce their principles. Businesses that implement strong governance frameworks, overseen by an ethics board and strengthened with appropriate training, will reduce the risks associated with AI.

ResearchGate, 2025

Artificial Intelligence (AI) is transforming modern society, offering significant advancements while raising profound ethical concerns. This paper examines key ethical issues, including algorithmic bias, privacy violations, accountability in autonomous systems, and economic disruptions due to automation. By analysing existing literature, case studies, and regulatory frameworks, we highlight critical risks such as algorithmic discrimination, data exploitation, and the socioeconomic impact of AI-driven job displacement. Furthermore, global regulatory efforts, including the European Union's AI Act, the UK's AI Strategy, and the fragmented policies in the United States, are assessed. The study argues that mitigating AI's ethical risks requires transparent algorithms, interdisciplinary governance approaches, and proactive policy interventions. Future considerations include AI's role in warfare, misinformation, environmental sustainability, healthcare, and human rights.

The debate about the ethical implications of Artificial Intelligence dates from the 1960s . However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Therefore, our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers 'apply ethics' at each stage of the pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.